Hierarchical decompositions for MPC of resource constrained control systems: applications to building energy management

Energy management can play a significant role in energy savings and temperature control of buildings, which consume a major share of energy resources worldwide. Model predictive control (MPC) has become a popular technique for energy management, arguably for its ability to cope with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single controller collecting signals and performing the computations. However, buildings are dynamic systems obtained by the interconnection of subsystems, with a distributed structure which is not necessarily explored by standard MPC. To this end, this work proposes hierarchical decompositions to split the computations between a master problem (centralized component) and a set of decoupled subproblems (distributed components) which brings about organizational flexibility and distributed computation. Three general methods are considered for hierarchical control and optimization, namely bilevel optimization, Benders and Lagrangean decomposition. Results are reported from a numerical analysis of the decompositions and a simulated application to the energy management of a building, in which a limited source of chilled water is distributed among HVAC units.